There’s a version of AI that answers questions. You prompt it, you get a response, you decide what to do next. Many financial services have been operating in that mode — and calling it transformation.
But agentic AI is something different. It doesn’t wait to be asked. It reasons, plans, and executes. One agent analyzes your current holdings. Another benchmarks similar portfolios against current market conditions. A third develops a rebalancing strategy. A fourth executes it and sends you a report. You weren’t in the loop for steps two through four. That’s not a chatbot. That’s a system making decisions on your behalf.
And that distinction matters more in finance than almost anywhere else.
This is what keeps risk officers at financial companies up at night. Traditional AI tells you something. You decide what to do. If the recommendation was wrong, there’s a human decision point in the chain. With agentic AI, that buffer disappears. An agent that autonomously denies loan applications without explainable reasoning, or one that executes trades during volatile conditions before a human can intervene — those aren’t hypotheticals anymore. They’re scenarios being stress-tested in compliance conversations right now.
The answer isn’t to slow down adoption. It’s to build differently. Governance and transparency can’t be retrofitted. They have to be architected into the system from day one — auditability, explainability, the ability to answer why a decision was made. If you can’t explain it, don’t ship it.
Tier your agents based on risk. An agent writing a summary report can run with minimal oversight. An agent executing large trades needs approval gates. The control you implement should match the stakes of the action — not the comfort level of whoever wants to ship fastest.
Everyone understands that AI needs data. The misconception is that you can point an agentic system at your existing data infrastructure and expect it to work. You can’t. Most of those systems weren’t built for this.
Traditional data infrastructure was built for humans running reports. Agentic AI is different — agents don’t make a few queries a day. They make hundreds of calls per minute. The gap between what your data stack was designed for and what agentic systems actually need is where most implementations stall.
In practice, the same three problems keep showing up.
Data quality. Many organizations manage costs by storing sampled or aggregated data. That was fine for monthly reporting. It’s not fine for real-time decisions. A fraud detection agent trained on sampled transaction data may miss the edge case it’s facing right now — because that edge case was never in the sample.
Accessibility. An agent can only respond as fast as it can query data. If your transaction history is in cold storage with a twelve-hour rehydration time, it doesn’t exist to the agent. By the time it’s retrieved, the transaction has cleared. Invisible data is the same as no data.
Fragmentation. Critical data lives in systems that don’t talk to each other. An agent assessing customer risk might need data from your CRM, your transaction system, and your support tickets — simultaneously. If that data is siloed, the agent is making decisions with an incomplete picture. In finance, an incomplete picture isn’t a minor inconvenience. It’s a liability.
More data without quality just gives the agent more noise to filter through. More data without accessibility is data that doesn’t exist. AI agents are not immune to garbage in, garbage out. Data quantity is table stakes. Quality, accessibility, and context are what determine whether your agents are fast decision engines or confidently wrong ones.
Traditional AI inference costs fractions of a cent. Agentic systems run ten to fifteen model calls per task — each querying data, validating, deciding. Each cycle can run ten cents to a dollar. Multiply by thousands of daily decisions. Then add monitoring, governance tooling, compliance overhead, and AI talent. The bill is frightening — and most teams didn’t model for it. Teams that go in assuming they can use existing infrastructure as-is are in for a rearchitecting.
Customers who have experienced seamless AI in consumer products now expect it from every piece of software they use. That pressure isn’t going away. Neither is the regulatory scrutiny.
The teams that navigate this well are the ones who audit their data capabilities before they start building — who understand where their data lives, whether it’s accessible via API, and what it would take to make it queryable in real time. The ones who build observability in as architecture, not as a feature added after the first incident will also sit head and shoulders above everyone else.
Agentic AI is only as smart as its data. Get the foundation right, and the speed follows. Skip it, and you’ll be explaining to a regulator why you can’t answer their question about a decision your system made six months ago.
That conversation does not go well.
Ashley Vassell is Senior Product Manager for Innovation Labs at Hydrolix, where she drives the company’s most forward-thinking initiatives beyond the core platform and console. Her work focuses on emerging capabilities like Anomaly Detection and MCP (Model Context Protocol), as well as integrations into third-party ecosystems including Spark, Splunk, Kibana, and Grafana.
